scholarly journals Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Bingbing Hu ◽  
Bing Li
Keyword(s):  
Stochastic Resonance ◽  
Crack Detection ◽  
Current Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Centrifugal Fan ◽  
Blade Vibration ◽  
Transform Method ◽  
Mode Decomposition ◽  
Centrifugal Fans ◽  
Blade Crack

Centrifugal fans are widely used in various industries as a kind of turbo machinery. Among the components of the centrifugal fan, the impeller is a key part because it is used to transform kinetic energy into pressure energy. Crack in impeller’s blades is one of the serious hidden dangers. It is important to detect the cracks in the blades as early as possible. Based on blade vibration signals, this research applies an adaptive stochastic resonance (ASR) method to diagnose crack fault in centrifugal fan. The ASR method, which can utilize the optimization ability of the grid search method and adaptively realize the optimal stochastic resonance system matching input signals, may weaken the noise and highlight weak characteristic and thus can diagnose the fault accurately. A centrifugal fan test rig is established and experiments with three cases of blades are conducted. In comparison with the ensemble empirical mode decomposition (EEMD) analysis and the traditional Fourier transform method, the experiment verified the effectiveness of the current method in blade crack detection.

scholarly journals Pressure Pulsation Signal Analysis for Centrifugal Compressor Blade Crack Determination

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Hongkun Li ◽  
Xuefeng Zhang ◽  
Xiaowen Zhang ◽  
Shuhua Yang ◽  
Fujian Xu
Keyword(s):  
Centrifugal Compressor ◽  
Early Warning ◽  
Signal Analysis ◽  
Pressure Pulsation ◽  
Process Analysis ◽  
Dynamic Strain ◽  
Blade Vibration ◽  
Mode Decomposition ◽  
Blade Failure ◽  
Blade Crack

Blade is a key piece of component for centrifugal compressor. But blade crack could usually occur as blade suffers from the effect of centrifugal forces, gas pressure, friction force, and so on. It could lead to blade failure and centrifugal compressor closing down. Therefore, it is important for blade crack early warning. It is difficult to determine blade crack as the information is weak. In this research, a pressure pulsation (PP) sensor installed in vicinity to the crack area is used to determine blade crack according to blade vibration transfer process analysis. As it cannot show the blade crack information clearly, signal analysis and empirical mode decomposition (EMD) are investigated for feature extraction and early warning. Firstly, signal filter is carried on PP signal around blade passing frequency (BPF) based on working process analysis. Then, envelope analysis is carried on to filter the BPF. In the end, EMD is carried on to determine the characteristic frequency (CF) for blade crack. Dynamic strain sensor is installed on the blade to determine the crack CF. Simulation and experimental investigation are carried on to verify the effectiveness of this method. The results show that this method can be helpful for blade crack classification for centrifugal compressors.

scholarly journals Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peiming Shi ◽  
Cuijiao Su ◽  
Dongying Han
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Good Effect ◽  
Resonance System ◽  
Mode Decomposition ◽  
Eemd Method

An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.

Optimizing the Adaptive Stochastic Resonance and Its Application in Fault Diagnosis

2015 ◽  
Vol 14 (04) ◽  
pp. 1550038 ◽  
Author(s):  
Xiaole Liu ◽  
Jianhua Yang ◽  
Houguang Liu ◽  
Gang Cheng ◽  
Xihui Chen ◽  
...  
Keyword(s):  
Stochastic Resonance ◽  
Resonance Method ◽  
Planetary Gear ◽  
Ensemble Empirical Mode Decomposition ◽  
Artificial Fish Swarm Algorithm ◽  
Mode Decomposition ◽  
Artificial Fish Swarm ◽  
Characteristic Signal ◽  
Weak Fault ◽  
Eemd Method

This paper presents an adaptive stochastic resonance method based on the improved artificial fish swarm algorithm. By this method, we can enhance the weak characteristic signal which is submerged in a heavy noise. We can also adaptively lead the stochastic resonance to be optimized to the greatest extent. The effectiveness of the proposed method is verified by both numerical simulation and lab experimental vibration signals including normal, a chipped tooth and a missing tooth of planetary gearboxes under the loaded condition. Both theoretical and experimental results show that this method can effectively extract weak characteristics in a heavy noise. In the experiment, each weak fault feature is extracted successfully from the fault planetary gear. When compared with the ensemble empirical mode decomposition (EEMD) method, the method proposed in this paper has been found to give remarkable performance.

scholarly journals Pulsation Signals Analysis of Turbocharger Turbine Blades based on Optimal EEMD and TEO

2019 ◽  
Vol 26 (3) ◽  
pp. 78-86
Author(s):  
Fengli Wang
Keyword(s):  
Turbine Blades ◽  
Energy Operator ◽  
Vibration Signal ◽  
Optimization Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Significant Feature ◽  
Intrinsic Mode Functions ◽  
Blade Vibration ◽  
Mode Decomposition ◽  
Nonlinear Signal

Abstract Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes nonlinear signal that is often encountered in turbomachinery research and development. An example of such signal is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient mode in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on optimal parameters of Ensemble Empirical Mode Decomposition (EEMD) and Teager Energy Operator (TEO) is proposed. First, an optimization method was designed for adaptive determining appropriate EEMD parameters for the measured vibration signal, so that the significant feature components can be extracted from the pulsating signals. Then Correlation Kurtosis (CK) is employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method.

scholarly journals Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Zhixing Li ◽  
Boqiang Shi
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Selection Of

A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.

scholarly journals Analysis method of the cavitation vibration signals in poppet valve based on EEMD

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

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